Abstract
Session-based recommendation aims to predict the next interaction in an anonymous user’s sequence and has gained significant attention. Most existing systems model user preferences from the current session using graph neural networks but overlook the varying importance of items with different popularity. To address this, we propose the Popularity-aware Graph Neural Network with Global Context (PGNN-GC), which models popularity features to better capture users’ diverse preferences. By explicitly modeling popularity-aware embeddings and using attention mechanisms, PGNN-GC differentiates user preferences for items of varying popularity. Additionally, we enhance representations using a contrastive learning paradigm. Experiments on three open datasets show that PGNN-GC achieves state-of-the-art performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding, C., Zhao, Z., Li, C., Yu, Y., Zeng, Q.: Session-based recommendation with hypergraph convolutional networks and sequential information embeddings. Expert Syst. Appl. 223, 119875 (2023)
Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)
Huang, X., Kou, Y., Shen, D., Nie, T., Li, D.: Exploiting item relationships with dual-channel attention networks for session-based recommendation. In: Yuan, L., Yang, S., Li, R., Kanoulas, E., Zhao, X. (eds.) Web Information Systems and Applications, WISA 2023, LNCS, vol. 14094, pp. 198–205. Springer, Singapore (2023). https://doi.org/10.1007/978-981-99-6222-8_17
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)
Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018)
Qiu, R., Li, J., Huang, Z., Yin, H.: Rethinking the item order in session-based recommendation with graph neural networks. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 579–588 (2019)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295 (2001)
Wang, Z., Wei, W., Cong, G., Li, X.L., Mao, X.L., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 169–178 (2020)
Wang, Z., et al.: Exploring global information for session-based recommendation. Pattern Recogn. 145, 109911 (2024)
Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)
Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4503–4511 (2021)
Zeyu, H., Yan, L., Wendi, F., Wei, Z., Alenezi, F., Tiwari, P.: Causal embedding of user interest and conformity for long-tail session-based recommendations. Inf. Sci. 644, 119167 (2023)
Acknowledgement
This work is supported in part by the National Natural Science Foundation of China under Grant 62377015, and the Collaborative Innovation Center for Intelligent Educational Technology of Guangzhou under grant 2023B04J0002.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zeng, X., Chang, C., Tang, F., Wu, Z., Tang, Y. (2024). Popularity-Aware Graph Neural Network with Global Context for Session-Based Recommendation. In: Jin, C., Yang, S., Shang, X., Wang, H., Zhang, Y. (eds) Web Information Systems and Applications. WISA 2024. Lecture Notes in Computer Science, vol 14883. Springer, Singapore. https://doi.org/10.1007/978-981-97-7707-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-97-7707-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-7706-8
Online ISBN: 978-981-97-7707-5
eBook Packages: Computer ScienceComputer Science (R0)